Impact of human disturbance on temporal partitioning within carnivore communities
Bibliographic record
Abstract
Abstract Interspecific competition is an important evolutionary force, influencing interactions between species and shaping the composition of biological communities. In mammalian carnivores, to reduce the risks of negative encounters between competitors, species can employ a strategy of temporal partitioning, adapting activity patterns to limit synchronous activity. This strategy of non‐human competitor avoidance, however, may be influenced by the expansion of human activities, which has driven wild mammals towards nocturnality. We hypothesise that the disruption of temporal niche partitioning by humans and their activities could increase temporal overlap between carnivores, enhancing interspecific competition. We reviewed the published literature systematically and employed generalised linear models to evaluate quantitatively the relative influence of a range of human, meteorological and ecological variables on coefficients of temporal overlap within mammalian terrestrial carnivore communities (orders Carnivora and Didelphimorphia) on a global scale. None of the models investigated showed evidence of an impact of humans on temporal partitioning between carnivores on a global scale. This illustrates that temporal avoidance of humans and competitors does not always follow a consistent pattern and that its strength may be context‐dependent and relative to other dimensions of niche partitioning (spatial and trophic). Similarly, the regulation of activity patterns may be strongly site‐specific and may be influenced by a combination of biotic and abiotic characteristics. Temporal avoidance of both humans and competitors by carnivores may take the form of short, reactive responses that do not impact activity patterns in the longer term. Although we did not detect a global disruption of temporal partitioning due to human disturbance, carnivore communities may still experience an increase in interspecific competition in other niche dimensions. Further research would benefit from using controlled experimental designs and investigating multiple dimensions of niche partitioning simultaneously. Finally, we recommend complementing the coefficient of temporal overlap with other metrics of fine‐scale spatiotemporal interactions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".